Sea Surface Temperature Index Research Articles

Unveiling of the SST gradient over the South China Sea and its possible relationship to the Asian precipitation

The present study examines the Sea Surface Temperature (SST) variations over the South China Sea (SCS) using the National Oceanic and Atmospheric Administration (NOAA) SST daily datasets for the period 1988–2009. In the SST over the South China Sea (SCS), a north-west versus south-east SST gradient (hereafter gra-SCS SST) is revealed. This SST gradient is seen to be induced by the distinct wind anomaly along with fresh water flux changes. The slope difference between the gra-SCS SST before and after 1999 is observed to be statistically significantly different than zero at a 95% confidence level. A decreasing/increasing trend in the gra-SCS SST is revealed before/after 1999. Besides, it is found that the gra-SCS SST characterizes the Asian monsoon variability by inducing cross equatorial monsoon flow towards the Asian Continent. In addition, we established the role of the gra-SCS SST in the Asian monsoon by computing causation based on the information flow concept between the gra-SCS SST and traditional monsoon indices, which pronounces that former impact the later. Based on the present corollary, we suggest that the gra-SCS SST should be considered as an important parameter in the climate studies as it is meticulously related to the precipitation over Asia.

The hydrological response of surface water to recent climate variability: A remote sensing case study from the central tropical Pacific

AbstractFor small tropical islands with limited freshwater resources, understanding how island hydrology is influenced by regional climate is important, considering projected hydroclimate and sea level changes as well as growing populations dependent on limited groundwater resources. However, the relationship between climate variability and hydrologic variability for many tropical islands remains uncertain due to local hydroclimatic data scarcity. Here, we present a case study from Kiritimati, Republic of Kiribati (2°N, 157°W), utilizing the normalized difference vegetation index to investigate variability in island surface water area, an important link between climate variability and groundwater storage. Kiritimati surface water area varies seasonally, following wet and dry seasons, and interannually, due to hydroclimate variability associated with the El Niño/Southern Oscillation. The NIÑO3.4 sea surface temperature index, satellite‐derived precipitation, precipitation minus evaporation, and local sea level all had significant positive correlations with surface water area. Lagged correlations show sea level changes and precipitation influence surface water area up to 6 months later. Differences in the timing of surface water area changes and variable climate‐surface water area correlations in island subregions indicate that surface hydrology on Kiritimati is not uniform in response to climate variations. Rather, the magnitude of the ocean–atmosphere anomalies and island–ocean connectivity determine the extent to which sea level and precipitation control surface water area. The very strong 2015–2016 El Niño event led to the largest surface water area measured in the 18‐year data set. Surface water area decreased to pre‐event values in a similarly rapid manner (<6 months) after both the very strong 2015–2016 event and the 2009–2010 moderate El Niño event. Future changes in the frequency and amplitude of interannual hydroclimate variability as well as seasonal duration will thus alter surface water coverage on Kiritimati, with implications for freshwater resources, flooding, and drought.

Weakening of the Senegalo–Mauritanian upwelling system under climate change

Upwelling processes bring nutrient-rich waters from the deep ocean to the surface. Areas of upwelling are often associated with high productivity, offering great economic value in terms of fisheries. The sensitivity of spring/summer-time coastal upwelling systems to climate change has recently received a lot of attention. Several studies have suggested that their intensity may increase in the future while other authors have shown decreasing intensity in their equatorward portions. Yet, recent observations do not show robust evidence of this intensification. The Senegalo-Mauritanian upwelling system (SMUS) located at the southern edge of the north Atlantic system (12°N–20°N) and most active in winter/spring has been largely excluded from these studies. Here, the seasonal cycle of the SMUS and its response to climate change is investigated in the database of the Coupled Models Inter comparison Project Phase 5 (CMIP5). Upwelling magnitude and surface signature are characterized by several sea surface temperature and wind stress indices. We highlight the ability of the climate models to reproduce the system, as well as their biases. The simulations suggest that the intensity of the SMUS winter/spring upwelling will moderately decrease in the future, primarily because of a reduction of the wind forcing linked to a northward shift of Azores anticyclone and a more regional modulation of the low pressures found over Northwest Africa. The implications of such an upwelling reduction on the ecosystems and local communities exploiting them remains very uncertain.

Temporal and spatial characteristics of droughts and floods in northern China from 1644 to 1911

This study analyses the temporal and spatial distributions of droughts and floods in northern China during the Qing Dynasty (1636–1912), with data ranging from 1644 to 1911, or nearly the entire dynastic period. Variations in the intensities of droughts and floods are investigated across different timescales and with respect to their spatial variation. The impacts and physical mechanisms of solar activity as well as surface temperatures of the Pacific Ocean on drought and flood intensities in northern China are analysed using a cross-wavelet method. The results reveal periodicities on scales of 10–11, 20–22, 45–50 and 90–100 yrs. Seven rapid changes are found on the 10-yr scale and four are found on the 30-yr scale. Analysing the spatial distribution of drought and flood intensities, we found a trend of decreasing precipitation from east to west. Precipitation over the Shandong region, which is in the eastern part of the study area, is significantly higher than that of the other regions, which may be explained by its coastal location and the resultant rich water vapour content of the atmosphere. Analyses of cross-wavelet power spectra and coherence spectra among the different drought and flood intensities and the Niño3 regional sea surface temperature (SST) index series and sunspot series indicate significant correlations between the drought and flood series and the Niño3 SST index series at scales of 2–4 and 8–12 yrs, and the phase difference differs across the different timescales. Furthermore, the drought and flood series and the sunspot series are closely correlated at a scale of 8–14 yrs.

Predictability of tropical vegetation greenness using sea surface temperatures**This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form

Much research has examined the sensitivity of tropical terrestrial ecosystems to various environmental drivers. The predictability of tropical vegetation greenness based on sea surface temperatures (SSTs), however, has not been well explored. This study employed fine spatial resolution remotely-sensed Enhanced Vegetation Index (EVI) and SST indices from tropical ocean basins to investigate the predictability of tropical vegetation greenness in response to SSTs and established empirical models with optimal parameters for hindcast predictions. Three evaluation metrics were used to assess the model performance, i.e., correlations between historical observed and predicted values, percentage of correctly predicted signs of EVI anomalies, and percentage of correct signs for extreme EVI anomalies. Our findings reveal that the pan-tropical EVI was tightly connected to the SSTs over tropical ocean basins. The strongest impacts of SSTs on EVI were identified mainly over the arid or semi-arid tropical regions. The spatially-averaged correlation between historical observed and predicted EVI time series was 0.30 with its maximum value reaching up to 0.84. Vegetated areas across South America (25.76%), Africa (33.13%), and Southeast Asia (39.94%) were diagnosed to be associated with significant SST-EVI correlations (p < 0.01). In general, statistical models correctly predicted the sign of EVI anomalies, with their predictability increasing from ∼60% to nearly 100% when EVI was abnormal (anomalies exceeding one standard deviation). These results provide a basis for the prediction of changes in greenness of tropical terrestrial ecosystems at seasonal to intra-seasonal scales. Moreover, the statistics-based observational relationships have the potential to facilitate the benchmarking of Earth System Models regarding their ability to capture the responses of tropical vegetation growth to long-term signals of oceanic forcings.

Variation of reference evapotranspiration and its teleconnection with multiple large‐scale climate oscillations in the Yangtze River Delta, China

Evapotranspiration is a key factor in regional hydrological processes and water resources management. Long‐term variation of evapotranspiration affects the regional climate wet/dry tendency and agricultural production profoundly. Thus, reference evapotranspiration (ET0) values for 46 meteorological stations in the Yangtze River Delta (YRD) were calculated for 1957–2014 using the FAO‐Penman–Monteith (FAO‐PM) method. The variation patterns of ET0 values were determined based on the principal component analysis (PCA) method. In addition, the methods of cross wavelet transform (CWT), Kendall tau‐b correlation coefficient determination and cross‐correlation method were applied in the assessment of the correlation between ET0 values and large‐scale climate oscillations, such as the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Oceanic Niño3.4 Sea Surface Temperature Index (NINO) and the Indian Ocean Dipole (IOD). Annual ET0 patterns for three dominant geographic subregions of the YRD (the southeastern, northwestern and mid‐eastern) were determined. There were only several discontinuous lower timescale bands between the three change patterns of annual ET0 and climate oscillations. In seasonal scale, the temporal patterns of ET0 changed simultaneously with the NAO, NINO and PDO in spring, the PDO in summer and the NAO in winter. The monthly ET0 was mostly influenced by the NAO and IOD in January, the IOD in February, the IOD and NINO in March, the NINO in June, the PDO in July to October, the NINO, IOD and NAO in October and the NINO in December. The lag times for the ET0 changes were about 0–5 months for the NAO and NINO, 1–2 months for the PDO and 4 months for the IOD.

Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach

Changes in mean sea level (MSL) are a major, but not the unique, cause of changes in high-percentile sea levels (HSL), e.g. the annual 99.9th quantile of sea level (among other factors, climate variability may also have huge influence). To unravel the respective influence of each contributor, we propose to use structural time series models considering six major climate indices (CI) (Artic Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Southern Oscillation Index, Niño 1 + 2 and Niño 3.4) as well as a reconstruction of MSL. The method is applied to eight century-long tide gauges across the world (Brest (France), Newlyn (UK), Cuxhaven (Germany), Stockholm (Sweden), Gedser (Danemark), Halifax (Canada), San Francisco (US), and Honolulu (US)). The treatment within a Bayesian setting enables to derive an importance indicator, which measures how often the considered driver is included in the model. The application to the eight tide gauges outlines that MSL signal is a strong driver (except for Gedser), but is not unique. In particular, the influence of Artic Oscillation index at Cuxhaven, Stockholm and Halifax, and of Niño Sea Surface Temperature index 1 + 2 at San Francisco appear to be very strong as well. A similar analysis was conducted by restricting the time period of interest to the 1st part of the 20th century. Over this period, we show that the MSL dominance is lower, whereas an ensemble of CI contribute to a large part to HSL time evolution as well. The proposed setting is flexible and could be applied to incorporate any alternative predictive time series such as river discharge, tidal constituents or vertical ground motions where relevant.

Using otolith chronologies to understand long‐term trends and extrinsic drivers of growth in fisheries

AbstractIdentifying trends and drivers of fish growth in commercial species is important for ongoing sustainable management, but there is a critical shortage of long‐term datasets in marine systems. Using otolith (ear bone) sclerochronology and mixed‐effects modeling, we reconstructed nearly four decades (37 yr) of growth across four oceanographically diverse regions in an iconic fishery species, snapper (Chrysophrys auratus). Growth was then related to environmental factors (sea surface temperature, chlorophyll‐a, and Southern Oscillation Index) and population performance indicators (recruitment and commercial catch). Across the decades, growth rates declined in the two most productive fishery regions. Chlorophyll‐a (a measure of primary productivity) was the best predictor of growth for all regions, but direction and magnitude of the relationships varied, indicating regional‐specific differences in intra‐specific competition. Sea surface temperature was positively correlated with fish growth, but negatively correlated after temperature reached optimum thermal maxima, which suggests individuals in warmer regions may be under thermal stress. Growth also decreased at the extremes of the Southern Oscillation Index, indicating fish growth is impeded in significant climatic events. Contrasting relationships between growth, catch, and recruitment indicated regional‐specific density‐dependent effects, with growth positively correlated with population size in one region but negatively correlated in another. Our results indicate that under future ocean warming and increased frequency of extreme climate events, fish growth and fisheries productivity are likely to be affected. Furthermore, the interactive effects of extrinsic factors also indicated that stressors on fisheries should be managed collectively. We show that otolith chronologies are an effective method to assess long‐term trends and drivers of growth in fishery species. Such informed ecological predictions will help shape the sustainable management of fisheries under future changing climates.

Analyzing the variation of the precipitation of coastal areas of eastern China and its association with sea surface temperature (SST) of other seas

In this paper, we analyzed the effect of SST on the precipitation of coastal areas of the eastern China. The relationship between the precipitation and SST of different seasons based on CMDC's monthly precipitation data and NOAA's SST data from 1979 to 2016 was calculated. Analysis on the links between precipitation and SST has shown a significant correlation in winter and spring, with three positive strong oceanic signals and two negatively correlated sea areas. Meanwhile, the precipitation and SST of highly correlated regions have increased during the past 38 years. Considering the seasonal variation, precipitation presents upward trend in summer and winter with appreciable seasonal variations. For one of the highly related areas, such as the East China Sea (ECS) and the north of Philippine Sea (PS), the SST there has been increased in all four seasons. Spectral analysis also shows that the precipitation and the MEI have the same cycles about 3–5 years. Moreover, lead-lag analysis between precipitation time-series and three climate indexes (Niño 1 + 2 SST index, Niño 3 SST index and SIOD SST index) were applied. The results show that when the SSTs lead 2 to 4 months, they are highly related to the precipitation in the coastal areas of the eastern China.

Assessing the dendroclimatic potential of Nothofagus betuloides (Magellan\u2019s beech) forests in the southernmost Chilean Patagonia

Key messageTree growth of Nothofagus betuloides forests south of 50°S is affected by local temperature and precipitation together with large-scale high-latitude climate patterns, but also significant influences from the Tropical Pacific were found.The characterization of past climate dynamics in southern South America is difficult due to the shortness of instrumental data. However, abundant forests in the southernmost part of the continent makes it an ideal place to assess the dendroclimatological potential for developing high-resolution climate proxy time series to extend the observations back in time. Whereas the majority of dendroclimatological studies have focused on latitudes north of 50°S, we present six new Magellan’s beech (Nothofagus betuloides (Mirb.) Oerst.) tree-ring width (TRW) chronologies, spanning between 202 and 500 years, developed from southernmost Patagonia (> 50°S), Chile. The climate signal in the trees was analyzed using local station data, regional sea surface temperature (SST) and large-scale atmospheric indices: the Southern Oscillation Index (SOI) and the southern annual mode (SAM). The correlations between individual site chronologies and observed summer temperatures and precipitation varied, reflecting diverse microsite conditions and local scale geographic patterns. An influence of southern Pacific SST was evident at two of the northern sites. Although the associations with SOI and SAM were weak, the influence of regional sea level pressure on tree growth in the region was evident.

Drought Characteristics and Its Response to the Global Climate Variability in the Yangtze River Basin, China

The frequent occurrence of drought events in humid and semi-humid regions is closely related to the global climate variability (GCV). In this study, the Standard Precipitation Evapotranspiration Index (SPEI) was taken as an index to investigate the drought in the Yangtze River Basin (YRB), a typical humid and semi-humid region in China. Furthermore, nine GCV indices, such as North Atlantic Oscillation (NAO) were taken to characterize the GCV. Correlation analysis and a joint probability distribution model were used to explore the relationship between the drought events and the GCV. The results demonstrated that there were six significant spatiotemporal modes revealed by SPEI3 (i.e., seasonal drought), which were consistent with the distribution of the main sub basins in the YRB, indicating a heterogeneity of drought regime. However, the SPEI12 (i.e., annual drought) can only reveal five modes. Precipitation Indices and El Niño/Southern Oscillation (ENSO) Indices were more closely related to the drought events. A causal relationship existed between ENSO precipitation index (ESPI), NAO, East Central Tropical Pacific Sea Surface Temperature (Nino3.4) and Northern Oscillation Index (NOI) and drought in the YRB, respectively. Drought events were most sensitive to the low NAO and high NOI events. This study shows a great significance for the understanding of spatiotemporal characteristics of meteorological drought and will provide a reference for the further formulation of water resources policy and the prevention of drought disasters.

The role of aerosols and greenhouse gases in Sahel drought and recovery

We exploit the multi-model ensemble produced by phase 5 of the Coupled Model Intercomparison Project (CMIP5) to synthesize current understanding of external forcing of Sahel rainfall change, past and future, through the lens of oceanic influence. The CMIP5 multi-model mean simulates the twentieth century evolution of Sahel rainfall, including the mid-century decline toward the driest years in the early 1980s and the partial recovery since. We exploit a physical argument linking anthropogenic emissions to the change in the temperature of the sub-tropical North Atlantic Ocean relative to the global tropical oceans to demonstrate indirect attribution of late twentieth century Sahel drought to the unique combination of aerosols and greenhouse gases that characterized the post-World War II period. The subsequent reduction in aerosol emissions around the North Atlantic that resulted from environmental legislation to curb acid rain, occurring as global tropical warming continued unabated, is consistent with the current partial recovery and with projections of future wetting. Singular Value Decomposition (SVD) applied to the above-mentioned sea surface temperature (SST) indices provides a succinct description of oceanic influence on Sahel rainfall and reveals the near-orthogonality in the influence of emissions between twentieth and twenty-first centuries: the independent effects of aerosols and greenhouse gases project on the difference of SST indices and explain past variation, while the dominance of greenhouse gases projects on their sum and explains future projection. This result challenges the assumption that because anthropogenic warming had a hand in past Sahel drought, continued warming will result in further drying. In fact, the twenty-first century dominance of greenhouse gases, unchallenged by aerosols, results in projections consistent with warming-induced strengthening of the monsoon, a response that has gained in coherence in CMIP5 compared to prior multi-model exercises.

Predicting wintertime windy days in Beijing with the preceding autumn sea surface temperature and large-scale circulation patterns

In this study, we investigate the role of sea surface temperature and large-scale circulation patterns in the preceding autumn for the prediction of wintertime windy days in Beijing during the period of 1979–2016, based on a time-scale decomposition approach. A windy day is defined according to the threshold of 2 m·s−1, which can effectively remove or reduce air pollution of Beijing. Wintertime windy days in Beijing, preceding autumn sea surface temperature and large-scale climate indices are separated into the interannual and interdecadal components. For the interannual component, the preceding autumn sea surface temperature in central western Pacific Ocean, Polar/Eurasia pattern, and Scandinavia pattern are identified as predictors of wintertime windy days in Beijing. For the interdecadal component, the identified predictors include the preceding autumn sea surface temperature in northeastern part of North Atlantic Ocean, Arctic Oscillation, and North Atlantic Oscillation. We develop statistical models for the interannual and interdecadal components based on these preceding autumn predictors, and take them together to predict wintertime windy days in Beijing. The leave-one-out cross-validation for 1979–2016 time series shows that 76% of the total variance of observed wintertime windy days in Beijing can be predicted. Hindcast experiments for the period of 1999–2016 also yield the high prediction skills. The physical mechanisms responsible for links between these predictors and wintertime windy days in Beijing are also discussed. The statistical models proposed herein are expected to be applied to and aid to improve seasonal forecasting of wintertime windy days in Beijing.

One-Month-Lead Predictability of Asian Summer Monsoon Indices Based on the Zonal Winds by the APCC Multimodel Ensemble

The seasonal predictability of Asian summer monsoon indices characterizing horizontal and vertical zonal wind shear is investigated using 1-month-lead hindcast datasets from seven coupled global circulation models (CGCMs) participating in the operational multimodel ensemble (MME) seasonal prediction system of the Asia–Pacific Economic Cooperation Climate Center (APCC) for the 1983–2010 period. The summer monsoon indices analyzed in this study represent the South Asian summer monsoon (SASM), western North Pacific summer monsoon (WNPSM), and the newly defined northeastern Asian summer monsoon (NEASM). For the WNPSM and NEASM indices, we also analyze the prediction skill of the index components separately. The study demonstrates that the operational APCC MME system reliably predicts most of the summer monsoon indices and their components, with correlation coefficients exceeding the 99% confidence level. Analysis of the ocean sources of the prediction skill of the indices reveals that the strong relationships of most of the monsoon indices and their components with sea surface temperature (SST) are not confined to the equatorial Pacific but rather are dispersed throughout the World Ocean, with the leading role played by the north Indian Ocean SST anomalies. This conclusion is supported by the analysis of correlations between the monsoon indices and the tropical SST indices. The correlations between the SST anomalies and all the summer monsoon indices in the MME predictions are stronger than those in the observations. However, overestimation of the role of the ENSO-related SST anomalies in the seasonal model hindcasts results in some predictability deterioration of the SASM and NEASM indices.

Identifying climatic factors and circulation indices related to apple yield variation in main production areas of China

Assessing the impacts of future climate change on crop yields requires basic research into both the major meteorological factors that affect crop yield and the influence that changes to global circulation patterns have on local meterological parameters. For example, the atmospheric circulation and crop “meteorological yield” and the response of meteorological yield to climate change are positively correlated. Using apple (Malus domestica) production as an example, the present work investigated the link between fruit yield and global circulation factors by using yield data from 28 apple-producing counties in Shaanxi province, China, from 1980 to 2015. We used four methods to isolate apple production and applied a Grey relational analysis of 88 meteorological factors and the associated sequential correlation. We extracted the major climate factors based on the high consistency of climatic factors and the variation trend of meteorological yield of apple, and analyzed the main meteorological factors for the meteorological apple yield of three major apple producing areas by analyzing high-impact atmospheric circulation indices. The climate factors included solar radiation, pan evaporation, accumulated temperature, precipitation, wind speed, maximum temperature, minimum temperature, mean temperature, heat injury over several days, number of frost days during flowering, number of freezing days during dormancy, and annual temperature range. The main meteorological factors affecting annual apple meteorological yield were total solar radiation from April to October, evaporation from April to September, precipitation in April and June to August, and minimum temperature in mid-April. The Southern Oscillation Index (SOI) was the circulation index most closely related to total summer solar radiation, pan evaporation, and precipitation. Correlation analysis showed that the polar vortex area index and sea surface temperature index were important circulation indices affecting climate yield in apple regions. The meteorological yields of apples in zones Ⅰ and Ⅲ were significantly correlated with the ENSO period index, mainly in the autumn and winter. Similar to the conclusions of previous studies, the uncertainty of meteorological yield in the Shaanxi fruit region is predicted to increase under future climate change.

Large‐scale indices for assessing typhoon activity around Taiwan

This study quantifies large‐scale indices associated with variations in the western Pacific subtropical high (WPSH) and tropical sea surface temperature (SST) to assess typhoon activity around Taiwan. A WPSH index is constructed for both summer and early autumn as the meridional difference of areal means between the northern (120°–130°E, 25°–30°N) and southern (120°–130°E, 20°–25°N) regions (north minus south) of the 500 hPa height. These are regions north and south of Taiwan. In the high index years, typhoons forming in the tropical western North Pacific tend to be steered by easterly/southeasterly flows at the southwestern boundary of a northwards‐shifted WPSH to move northwestwards towards Taiwan, leading to increased typhoon activity around Taiwan. In the low index years, a southwards‐shifted, eastwards‐retreated WPSH steers typhoons along its western periphery to move northwards away from Taiwan. The northwards shift of the WPSH and consequent enhanced typhoon activity around Taiwan associate with a meridional wave train induced by a zonal SST gradient in summer, but link with a Matsuno‐Gill‐type response induced by tropical SSTs in early autumn. As so, the summer SST index is defined as a zonal gradient of areal‐mean SSTs between the tropical western Pacific (150°–175°E, 10°–20°N) and the Bay of Bengal–South China Sea–Philippine Sea region (80°–130°E, 5°–20°N) (former minus latter). In early autumn, the SST index is averaged from the tropical western Pacific over 130°–165°E, 5°S–5°N. All WPSH and SST indices in summer and early autumn are significantly correlated (at the 99% level) with the number of typhoons affecting Taiwan.

Predictability of phases and magnitudes of natural decadal climate variability phenomena in CMIP5 experiments with the UKMO HadCM3, GFDL-CM2.1, NCAR-CCSM4, and MIROC5 global earth system models

Data from decadal hindcast experiments conducted under CMIP5 were used to assess the ability of CM2.1, HadCM3, MIROC5, and CCSM4 Earth System Models (ESMs) to hindcast sea-surface temperature (SST) indices of the Pacific Decadal Oscillation (PDO), the tropical Atlantic SST gradient (TAG) variability, and the West Pacific Warm Pool (WPWP) SST variability from 1961 to 2010. The ESMs were initialized at specific times with observed data to make 10- and 30-year hindcasts/forecasts. Deterministic and probabilistic skill estimates show predictability of detrended WPWP index to 5 years’ lead time and of non-detrended WPWP index to 10 years’ lead time. These estimates also show atypical skill dependence of PDO and TAG indices on lead times, with increasing skill in the middle to end of 10-year hindcasts. The skill of ESMs to hindcast an observed DCV index (signal skill) is surprisingly greater than the skill to hindcast their own DCV index (noise skill) at some lead times. All ESMs hindcast occurrence frequencies of positive and negative phases of the indices, and probabilities of same-phase transitions from one year to the next reasonably well. Four, major, low-latitude volcanic eruptions are associated with phase transitions of all observed and some of the ensemble-average hindcast indices. All ESMs’ WPWP index hindcasts respond correctly to all eruptions as do three observed PDO phase transitions. No one of the ESMs’ hindcasts of the TAG index responds correctly to these eruptions. Some of the ESMs hindcast correct phase transitions in the absence of eruptions also, implying that initializations with observed data are beneficial in predicting phase transitions. The skills of DCV indices’ phase prediction up to at least two years in advance can be used to inform societal impacts adaptation decisions in water resources management and agriculture. The Atlantic region’s responses in these ESMs appear to be fundamentally incorrect.

The Influence of Indian Ocean Dipole (IOD) on The Variability of Sea Surface Temperature and Precipitation in Sumatera Island

Sumatera Island is located adjacent to the ocean and directly influenced by the dynamic of Indian Ocean. Indian Ocean Dipole (IOD) as one of the global climate factor has a big impact towards this dynamic. As it aims to get the influence of IOD towards Sumatera water area, high-resolution remote sensing data is needed. Those are Sea Surface Temperature (SST), precipitation, and Dipole Mode Index (DMI). DMI is calculated by the differentiation of SST anomaly. IOD condition is emphasized by using precipitation data. Area observed is divided into three areas which are in series West Sumatera water, East Sumatera water, and Bukit Barisan highland. During IOD negative, the precipitation values were estimated 5,43; 4,63; 4,5 mm/hr sequentially. Meanwhile, during IOD positive, those values were 2,74; 3,47; 3,11 mm/hr sequentially. It is shown that IOD has the impact of the precipitation values in Sumatera waters. By showing the higher value of IOD negative and the lower value on IOD positive. West Sumatera water is observed as the most influencing area by the dynamic of IOD. Topography is also influencing towards the precipitation and it is shown by its values weather on IOD negative or positive.

Predicting summer monsoon of Bhutan based on SST and teleconnection indices

The paper uses a statistical method of predicting summer monsoon over Bhutan using the ocean-atmospheric circulation variables of sea surface temperature (SST), mean sea-level pressure (MSLP), and selected teleconnection indices. The predictors are selected based on the correlation. They are the SST and MSLP of the Bay of Bengal and the Arabian Sea and the MSLP of Bangladesh and northeast India. The Northern Hemisphere teleconnections of East Atlantic Pattern (EA), West Pacific Pattern (WP), Pacific/North American Pattern, and East Atlantic/West Russia Pattern (EA/WR). The rainfall station data are grouped into two regions with principal components analysis and Ward’s hierarchical clustering algorithm. A support vector machine for regression model is proposed to predict the monsoon. The model shows improved skills over traditional linear regression. The model was able to predict the summer monsoon for the test data from 2011 to 2015 with a total monthly root mean squared error of 112 mm for region A and 33 mm for region B. Model could also forecast the 2016 monsoon of the South Asia Monsoon Outlook of World Meteorological Organization (WMO) for Bhutan. The reliance on agriculture and hydropower economy makes the prediction of summer monsoon highly valuable information for farmers and various other sectors. The proposed method can predict summer monsoon for operational forecasting.

Recent predictors of Indian summer monsoon based on Indian and Pacific Ocean SST

This study investigates the relationship between sea surface temperature (SST) of various geographical locations of Indian and Pacific Ocean with the Indian summer monsoon rainfall (ISMR) to identify possible predictors of ISMR. We identified eight SST predictors based on spatial patterns of correlation coefficients between ISMR and SST of the regions mentioned above during the time domain 1982–2013. The five multiple linear regression (MLR) models have been developed by these predictors in various combinations. The stability and performance of these MLR models are verified using cross-validation method and other statistical methods. The skill of forecast to predict observed ISMR from these MLR models is found to be substantially better based on various statistical verification measures. It is observed that the MLR models constructed using the combination of SST indices in tropical and extra tropical Indian and Pacific is able to predict ISMR accurately for almost all the years during the time domain of our study. We tried to propose the physical mechanism of the teleconnection through regression analysis with wind over Indian subcontinent and the eight predictors and the results are in the conformity with correlation coefficient analysis. The robustness of these models is seen by predicting the ISMR during recent independent years of 2014–2017 and found the model 5 is able to predict ISMR accurately in these years also.